Marc Bocquet1, Alberto Carrassi2, Chris KRT Jones 3 et al.
I have been an Assistant Professor of Statistics at the University of Nevada, Reno since January 2019;
Despite the challenges, I have pushed forward an innovative research and teaching program by utilizing my core strengths:
In addition to my technical experience, I am highly collaborative and I enjoy the camaraderie and sense of purpose in working as part of a tight-knit team.
I will share how my experience and accomplishments demonstrate these skills;
My research focus is on scalable data assimilation methodology;
I utilize dynamical, statistical and numerical tools for understanding:
Originally trained in pure mathematics, I approach my work by leveraging my mathematical training and by continually expanding the depth and breadth of my expertise.
My dissertation studied stability analysis of special solutions to PDEs utilizing geometric and dynamical systems tools.1,2
I proceeded to leverage my training in dynamical systems to develop a novel stability analysis of the Kalman filter and the EnKF.
Numerical and empirical results have long demonstrated that the skill of ensemble DA methods in chaotic systems is strongly related to dynamic instabilities.4,5,6
Trevisan et al. proposed filtering methodology for dimensional reduction called Assimilation in the Unstable Subspace (AUS).8
AUS gave an intuitive explanation for results in targeting observations to constrain forecast error growth.
However, AUS lacked a mathematical formalism that would allow these results to extend beyond the “perfect” model assumption in DA.
In my PhD, my collaborators and I established the fundamental mathematical theory for these dimensional reduction results.
My future research interests include extending the SIEnKS formalism to realistic short-range prediction settings.
A growing opportunity in short-range and now-casting is in the use of data-driven prediction systems with deep learning;19
Rather than replacing dynamical models entirely, growing research indicates a path forward for a hybrid approach:
The hybrid approach, furthermore, can use dynamical principles for data reconnaissance.
This research program runs parallel with a longer-term book project I am developing with my collaborators.22
My research covers most aspects of the theoretical DA problem, from:
Likewise, my work experience is adjacent to the operational aspects of the problem, including:
Additionally, my teaching skills provide me with
I believe the skill-base that I bring is complementary with the existing operational strengths of the institute;
Likewise, I am eager to utilize my core strengths to develop novel techniques for hybrid DA-machine learning in short-range prediction cycles.